OntoNotes : A Large Training Corpus for Enhanced Processing

By Ralph M. Weischedel, Eduard H. Hovy, Mitchell P. Marcus, Martha Palmer. 2017

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models (2021, background): “…and was attributed to the inherent bias of the underlying word embeddings (Bolukbasi et al., 2016), as well as the training of these coreference resolution pipelines on the OntoNotes 5.0 dataset (Weischedel et al., 2011) which is known to suffer from severe gender imbalance (Zhao et al., 2018a).”
Transforming Dependency Structures to Logical Forms for Semantic Parsing (2016, methodology): “The tagger and parser are both trained on the OntoNotes 5.0 corpus (Weischedel et al., 2011), with constituency trees converted to Stanford-style dependencies (De Marneffe and Manning, 2013).”
Efficient Inference and Structured Learning for Semantic Role Labeling (2015, methodology): “We further present results on the OntoNotes 5.0 corpora annotated with semantic roles for both verbal and nominal predicates (Weischedel et al., 2011) and strongly outperform the prior state of the art (Pradhan et al., 2013).”
Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation (2016, background): “Unrestricted event coreference annotations were later developed in OntoNotes (Weischedel et al., 2011) – which annotated event coreference but did not explicitly differentiate events and entities – and in cross-document event corpora such as Lee et al. (2012), Cybulska and Vossen (2014), Minard et a”
Intrinsic Bias Metrics Do Not Correlate with Application Bias (2020, methodology): “8 Our English Corefer-ence system is trained on OntoNotes (Weischedel et al., 2017) and evaluated on Winobias (Zhao et al., 2018a), a Winograd-schema style challenge set designed to measure gender bias in coreference resolution.”
We Need to Talk about Standard Splits (2019, methodology): “In experiment 1, we also use a portion of OntoNotes 5 (LDC2013T19; Weischedel et al., 2011), a substantial subset of the Penn Treebank WSJ data re-annotated for quality assurance.”
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, methodology): “To achieve this, we annotated 100 examples uniformly at random by hand from OntoNotes (Weischedel et al. 2011).”
The NLP Task Effectiveness of Long-Range Transformers (2022, background): “(Weischedel et al., 2011) and extractive question answering (eQA) (Joshi et al., 2017); 2) sequence classiﬁcation : natural language inference (NLI) (Yin et al., 2021); 3 and 3) seq2seq : summarization (Summ.)”
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding (2016, methodology): “For Wiki and OntoNotes datasets, we used the provided training/test set partitions of the corpora.”
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (2019, methodology): “0 corpus7 when training the dependency parser and part of speech tagger (Weischedel et al., 2011; Hovy et al., 2006).”
How Does BERT Answer Questions?: A Layer-Wise Analysis of Transformer Representations (2019, background): “0 corpus [38] and contain 18 entity categories.”
Training Complex Models with Multi-Task Weak Supervision (2018, methodology): “…fine-grained named entity recognition problem— tagging entity mentions in text documents—as a hierarchy of three sub-tasks over the OntoNotes dataset (Weischedel et al. 2011): Y1 ∈ {Person,Organization}, Y2 ∈ {Businessperson,Other Person,N/A}, Y3 ∈ {Company,Other Org,N/A}, where again we use N/A…”